The Annotation Game: On Turing (1950) on Computing, Machinery, and Intelligence

This quote/commented critique of Turing's classical paper suggests that Turing meant -- or should have meant -- the robotic version of the Turing Test (and not just the email version). Moreover, any dynamic system (that we design and understand) can be a candidate, not just a computational one. Turing also dismisses the other-minds problem and the mind/body problem too quickly. They are at the heart of both the problem he is addressing and the solution he is proposing

Validation of Expert Systems: Personal Choice Expert -- A Flexible Employee Benefit System

A method for validating expert systems, based on psychological validation literature and Turing\u27s imitation game, is applied to a flexible benefits expert system. Expert system validation entails determining if a difference exists between expert and novice decisions (construct validity), if the system uses the same inputs and processes to make its decisions as experts (content validity), and if the system produces the same results as experts (criterionrelated validity). If these criteria are satisfied, then the system is indistinguishable from experts for its domain and satisfies Turing\u27s imitation game. The methods developed in this paper are applied to a human resource expert system, Personal Choice Expert (PCE), designed to help employees choose a benefits package in a flexible benefits system. Expert and novice recommendations are compared to those generated by PCE. PCE\u27s recommendations do not significantly differ from those given by experts. High inter-expert agreement exists for some benefit recommendations (e.g. Dental Care and Long-Term Disability) but not for others (e.g. Short-Term Disability and Life Insurance). Insights offered by this method are illustrated and examined

The Turing Deception

This research revisits the classic Turing test and compares recent large language models such as ChatGPT for their abilities to reproduce human-level comprehension and compelling text generation. Two task challenges -- summarization, and question answering -- prompt ChatGPT to produce original content (98-99%) from a single text entry and also sequential questions originally posed by Turing in 1950. We score the original and generated content against the OpenAI GPT-2 Output Detector from 2019, and establish multiple cases where the generated content proves original and undetectable (98%). The question of a machine fooling a human judge recedes in this work relative to the question of "how would one prove it?" The original contribution of the work presents a metric and simple grammatical set for understanding the writing mechanics of chatbots in evaluating their readability and statistical clarity, engagement, delivery, and overall quality. While Turing's original prose scores at least 14% below the machine-generated output, the question of whether an algorithm displays hints of Turing's truly original thoughts (the "Lovelace 2.0" test) remains unanswered and potentially unanswerable for now

The Turing test as interactive proof

In 1950, Alan Turing proposed his eponymous test based on indistinguishability of verbal behavior as a replacement for the question "Can machines think?" Since then, two mutually contradictory but well-founded attitudes towards the Turing Test have arisen in the philosophical literature. On the one hand is the attitude that has become philosophical conventional wisdom, viz., that the Turing Test is hopelessly flawed as a sufficient condition for intelligence, while on the other hand is the overwhelming sense that were a machine to pass a real live full-fledged Turing Test, it would be a sign of nothing but our orneriness to deny it the attribution of intelligence. The arguments against the sufficiency of the Turing Test for determining intelligence rely on showing that some extra conditions are logically necessary for intelligence beyond the behavioral properties exhibited by an agent under a Turing Test. Therefore, it cannot follow logically from passing a Turing Test that the agent is intelligent. I argue that these extra conditions can be revealed by the Turing Test, so long as we allow a very slight weakening of the criterion from one of logical proof to one of statistical proof under weak realizability assumptions. The argument depends on the notion of interactive proof developed in theoretical computer science, along with some simple physical facts that constrain the information capacity of agents. Crucially, the weakening is so slight as to make no conceivable difference from a practical standpoint. Thus, the Gordian knot between the two opposing views of the sufficiency of the Turing Test can be cut.Engineering and Applied Science

Inverting the Turing Test [review of The Most Human Human by Brian Christian]

In his book The Most Human Human, Brian Christian extrapolates from his experiences at the 2009 Loebner Prize competition, a competition among chatbots (computer programs that engage in conversation with people) to see which is "most human." In doing so, he demonstrates once again that the human being may be the only animal that overinterprets.Engineering and Applied Science

Computers and the Nature of Man: A Historian\u27s Perspective on Controversies about Artificial Intelligence

The purpose of the present paper is to provide a historical perspective on recent controversies, from Turing\u27s time on, about artificial intelligence, and to make clear that these are in fact controversies about the nature of man. First, I shall briefly review three recent controversies about artificial intelligence, controversies over whether computers can think and over whether people are no more than information-processing machines. These three controversies were each initiated by philosophers who, irrespective of what the programs of their time actually did, viewed with alarm the argument that if a machine can think, a thinking being is just a machine. I will then turn to the major business of this paper: to contrast two developments from within the field of AI which have been interpreted by some as successful steps toward simulating human thought, and also to contrast some reactions to that claimed success. Finally, we will look at some recent developments in the field of AI that suggest that the whole discussion about machine intelligence is at best premature and at worst irrelevant

You Might be a Robot

As robots and artificial intelligence (Al) increase their influence over society, policymakers are increasingly regulating them. But to regulate these technologies, we first need to know what they are. And here we come to a problem. No one has been able to offer a decent definition of robots arid AI-not even experts. What\u27s more, technological advances make it harder and harder each day to tell people from robots and robots from dumb machines. We have already seen disastrous legal definitions written with one target in mind inadvertently affecting others. In fact, if you are reading this you are (probably) not a robot, but certain laws might already treat you as one. Definitional challenges like these aren\u27t exclusive to robots and Al. But today, all signs indicate we are approaching an inflection point. Whether it is citywide bans of robot sex brothels or nationwide efforts to crack down on ticket scalping bots, we are witnessing an explosion of interest in regulating robots, human enhancement technologies, and all things in between. And that, in turn, means that typological quandaries once confined to philosophy seminars can no longer be dismissed as academic. Want, for example, to crack down on foreign influence campaigns by regulating social media bots? Be careful not to define bot too broadly (like the Calfornia legislature recently did), or the supercomputer nestled in your pocket might just make you one. Want, instead, to promote traffic safety by regulating drivers? Be careful not to presume that only humans can drive (as our Federal Motor Vehicle Safety Standards do), or you may soon exclude the best drivers on the road. In this Article, we suggest that the problem isn\u27t simply that we haven\u27t hit upon the right definition. Instead, there may not be a right definition for the multifaceted, rapidly evolving technologies we call robots or AI. As we will demonstrate, even the most thoughtful of definitions risk being overbroad, underinclusive, or simply irrelevant in short order. Rather than trying in vain to find the perfect definition, we instead argue that policymakers should do as the great computer scientist, Alan Turing, did when confronted with the challenge of defining robots: embrace their ineffable nature. We offer several strategies to do so. First, whenever possible, laws should regulate behavior, not things (or as we put it, regulate verbs, not nouns). Second, where we must distinguish robots from other entities, the law should apply what we call Turing\u27s Razor, identifying robots on a case-by-case basis. Third, we offer six functional criteria for making these types of I know it when I see it determinations and argue that courts are generally better positioned than legislators to apply such standards. Finally, we argue that if we must have definitions rather than apply standards, they should be as short-term and contingent as possible. That, in turn, suggests that regulators-not legislators-should play the defining role